{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Metrics\n",
    "Most modern circulation models discretize the partial differential equations needed to simulate the earth system on a logically rectangular grid. This means the grid for a single time step can be represented as a 3-dimensional array of cells. Even for more complex grid geometries like [here](https://xgcm.readthedocs.io/en/latest/grid_topology.html), subdomains are usually organized in this manner. A noteable exception are models with unstructured grids [example](https://en.wikipedia.org/wiki/Unstructured_grid), which currently cannot be processed with the datamodel of xarray and xgcm.\n",
    "\n",
    "Our grid operators work on the logically rectangular grid of an ocean model, meaning that e.g. differences are evaluated on the 'neighboring' cells in either direction, but even though these cells are adjacent, cells can have different size and geometry.\n",
    "\n",
    "In order to convert operators acting on the logically rectangular grid to physically meaningful output models need 'metrics' - information about the grid cell geometry in physical space.\n",
    "In the case of a perfectly [rectangular cuboid](https://en.wikipedia.org/wiki/Cuboid), the only metrics needed would be three of the edge distances. All other distances can be reconstructed exactly. Most ocean models have however slightly distorted cells, due to the curvature of the earth. To accurately represent the volume of the cell we require more metrics. \n",
    "\n",
    "Each grid point has three kinds of fundamental metrics associated with it which differ in the number of described axes:\n",
    "\n",
    "1. **Distances**: A distance is associated with a single axis (e.g. `('X',)`,`('Y',)` or `('Z',)`). Each distance describes the distance from the point to either face of the cell associated with the grid point. \n",
    "2. **Areas**: An area is associated with a pair of axes (e.g. `('X', 'Y')`, `('Y', 'Z')` and `('X', 'Z')`). Each grid point intersects three areas.\n",
    "3. **Volume**: The cell volume is unique for each cell and associated with all three axes (`('X', 'Y', 'Z')`).\n",
    "\n",
    "## Using metrics with xgcm\n",
    "Once the user assigns the metrics (given as coordinates in most model output) to the `grid` object, xgcm is able to automatically select and apply these to calculate e.g. derivatives and integrals from model data. \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "*Note*: xgcm does not currently check for alignment of missing values between data and metrics. The user needs to check and mask values appropriately\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tim/anaconda3/lib/python3.7/site-packages/docrep/__init__.py:413: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  s = dedents(s)\n",
      "/Users/tim/anaconda3/lib/python3.7/site-packages/docrep/__init__.py:342: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  s = dedents('\\n' + '\\n'.join(lines[first:]))\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from xgcm import Grid\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (XC: 90, XG: 90, YC: 40, YG: 40, Z: 15, Zl: 15, time: 1)\n",
       "Coordinates:\n",
       "  * time     (time) timedelta64[ns] 11:00:00\n",
       "    maskC    (Z, YC, XC) bool ...\n",
       "    dyC      (YG, XC) float32 ...\n",
       "    hFacC    (Z, YC, XC) float32 ...\n",
       "    rA       (YC, XC) float32 ...\n",
       "    hFacS    (Z, YG, XC) float32 ...\n",
       "    Depth    (YC, XC) float32 ...\n",
       "  * YG       (YG) float32 -80.0 -76.0 -72.0 -68.0 -64.0 ... 64.0 68.0 72.0 76.0\n",
       "  * Z        (Z) float32 -25.0 -85.0 -170.0 -290.0 ... -3575.0 -4190.0 -4855.0\n",
       "    PHrefC   (Z) float32 ...\n",
       "    dyG      (YC, XG) float32 ...\n",
       "    rAw      (YC, XG) float32 ...\n",
       "    drF      (Z) float32 ...\n",
       "  * YC       (YC) float32 -78.0 -74.0 -70.0 -66.0 -62.0 ... 66.0 70.0 74.0 78.0\n",
       "    dxG      (YG, XC) float32 ...\n",
       "  * XG       (XG) float32 0.0 4.0 8.0 12.0 16.0 ... 344.0 348.0 352.0 356.0\n",
       "    iter     (time) int64 ...\n",
       "    maskW    (Z, YC, XG) bool ...\n",
       "  * Zl       (Zl) float32 0.0 -50.0 -120.0 -220.0 ... -3280.0 -3870.0 -4510.0\n",
       "    rAs      (YG, XC) float32 ...\n",
       "    rAz      (YG, XG) float32 ...\n",
       "    maskS    (Z, YG, XC) bool ...\n",
       "    dxC      (YC, XG) float32 ...\n",
       "    hFacW    (Z, YC, XG) float32 ...\n",
       "  * XC       (XC) float32 2.0 6.0 10.0 14.0 18.0 ... 346.0 350.0 354.0 358.0\n",
       "Data variables:\n",
       "    UVEL     (time, Z, YC, XG) float32 ...\n",
       "    VVEL     (time, Z, YG, XC) float32 ...\n",
       "    WVEL     (time, Zl, YC, XC) float32 ...\n",
       "    SALT     (time, Z, YC, XC) float32 ...\n",
       "    THETA    (time, Z, YC, XC) float32 ...\n",
       "    PH       (time, Z, YC, XC) float32 ...\n",
       "    Eta      (time, YC, XC) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        netCDF wrapper of MITgcm MDS binary data\n",
       "    source:       MITgcm\n",
       "    history:      Created by calling `open_mdsdataset(extra_metadata=None, ll..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hack to make file name work with nbsphinx and binder\n",
    "import os\n",
    "fname = '../datasets/mitgcm_example_dataset_v2.nc'\n",
    "if not os.path.exists(fname):\n",
    "    fname = '../' + fname\n",
    "    \n",
    "ds = xr.open_dataset(fname)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For mitgcm output we need to first incorporate partial cell thicknesses into new metric coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['drW'] = ds.hFacW * ds.drF #vertical cell size at u point\n",
    "ds['drS'] = ds.hFacS * ds.drF #vertical cell size at v point\n",
    "ds['drC'] = ds.hFacC * ds.drF #vertical cell size at tracer point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assign the metrics, the user has to provide a dictionary with keys and entries corresponding to the spatial orientation of the metrics and a list of the appropriate variable names in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    ('X',): ['dxC', 'dxG'], # X distances \n",
    "    ('Y',): ['dyC', 'dyG'], # Y distances \n",
    "    ('Z',): ['drW', 'drS', 'drC'], # Z distances \n",
    "    ('X', 'Y'): ['rA', 'rAz', 'rAs', 'rAw'] # Areas \n",
    "}\n",
    "grid = Grid(ds, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid aware integration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now possible to integrate over any grid axis. For example, we can integrate over the Z axis to compute the \n",
    "discretized version of:\n",
    "\n",
    "$$ \\int_{-H}^0 u dz$$\n",
    "\n",
    "in one line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.integrate(ds.UVEL, 'Z').plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is equivalent to doing `(ds.UVEL * ds.drW).sum('Z')`, with the advantage of not having to remember the name of the appropriate metric and the matching dimension. The only thing the user needs to input is the axis to integrate over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = grid.integrate(ds.UVEL, 'Z')\n",
    "b = (ds.UVEL * ds.drW).sum('Z')\n",
    "xr.testing.assert_equal(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the exact same thing on a tracer field (which is located on a different grid point) by using the exact same syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.integrate(ds.SALT, 'Z').plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also works in two dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = grid.integrate(ds.UVEL, ['X', 'Y'])\n",
    "a.plot(y='Z')\n",
    "\n",
    "# Equivalent to integrating over area\n",
    "b = (ds.UVEL * ds.rAw).sum(['XG', 'YC'])\n",
    "xr.testing.assert_equal(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally in 3 dimensions, this time using the salinity of the tracer cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spatial integral of zonal velocity:  [2.3674485e+15]\n"
     ]
    }
   ],
   "source": [
    "print('Spatial integral of zonal velocity: ',grid.integrate(ds.UVEL, ['X', 'Y', 'Z']).values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But wait, we did not provide a cell volume when setting up the `Grid`. What happened?\n",
    "\n",
    "Whenever no matching metric is provided, xgcm will default to reconstruct it from the other available metrics, in this case the area and z distance of the tracer cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = grid.integrate(ds.SALT, ['X', 'Y', 'Z'])\n",
    "b = (ds.SALT * ds.rA * ds.drC).sum(['XC', 'YC', 'Z'])\n",
    "xr.testing.assert_allclose(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid-aware (weighted) average\n",
    "\n",
    "xgcm can also calcualate the weighted average along each axis and combinations of axes. \n",
    "See for example the vertical average of salinity:\n",
    "\n",
    "$$ \\frac{\\int_{-H}^0 S dz}{\\int_{-H}^0 dz} $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth mean salinity\n",
    "grid.average(ds.SALT, ['Z']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equivalently, this can be computed with the xgcm operations:\n",
    "\n",
    "```\n",
    "(ds.SALT * ds.drF).sum('Z') / ds.drF.sum('Z')\n",
    "```\n",
    "\n",
    "See also for zonal velocity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depth mean zonal velocity\n",
    "grid.average(ds.UVEL, ['Z']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works with multiple dimensions as well: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal average zonal velocity\n",
    "grid.average(ds.UVEL, ['X','Y']).plot(y='Z');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume weighted average of salinity:  [34.73095]\n"
     ]
    }
   ],
   "source": [
    "# average salinity of the global ocean\n",
    "# horizontal average zonal velocity\n",
    "print('Volume weighted average of salinity: ',grid.average(ds.SALT, ['X','Y', 'Z']).values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative integration\n",
    "\n",
    "Using the metric-aware cumulative integration `cumint`, we can calculate the [barotropic transport streamfunction](https://xgcm.readthedocs.io/en/latest/example_mitgcm.html#Barotropic-Transport-Streamfunction) even easier and more intuitive in one line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the streamfunction is the cumulative integral of the vertically integrated zonal velocity along y\n",
    "psi = grid.cumint(-grid.integrate(ds.UVEL,'Z'),'Y', boundary='fill')\n",
    "\n",
    "maskZ = grid.interp(ds.hFacS, 'X').isel(Z=0)\n",
    "(psi / 1e6).squeeze().where(maskZ).plot.contourf(levels=np.arange(-160, 40, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, `cumint` is performing the discretized form of:\n",
    "\n",
    "$$ \\psi = \\int_{y_0}^{y} -U  dy' $$\n",
    "\n",
    "where $U = \\int_{-H}^0 u dz$, and under the hood looks like the following operation: \n",
    "\n",
    "```\n",
    "grid.cumsum( -grid.integrate(ds.UVEL,'Z') * ds.dyG, 'Y', boundary='fill')\n",
    "```\n",
    "\n",
    "Except that, once again, one does not have to remember the matching metric while using `cumint`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing derivatives\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a similar fashion to integration, xgcm uses `metrics` to compute derivatives. \n",
    "For this example we show vertical shear, i.e. the derivative of some quantity in the vertical.\n",
    "\n",
    "At it's core, `derivative` is based on `diff`, which shifts a data array \n",
    "to a new grid point, as shown\n",
    "[here](https://xgcm.readthedocs.io/en/latest/grids.html#core-grid-operations-diff-interp-and-cumsum).\n",
    "Because of this shifting, we need to either define new metrics which live at the right points on the grid, \n",
    "or first interpolate the desired quantities, anticipating the shift.\n",
    "Here we choose the latter, and interpolate velocities and temperature onto the vertical cell faces of the grid \n",
    "cells.\n",
    "The resulting quantities are in line with the vertical velocity `w`, which is shown in the vertical grid of the C \n",
    "grid [here](https://xgcm.readthedocs.io/en/latest/grids.html#general-concepts)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "uvel_l = grid.interp(ds.UVEL,'Z')\n",
    "vvel_l = grid.interp(ds.VVEL,'Z')\n",
    "theta_l = grid.interp(ds.THETA,'Z')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The subscript \"l\" is used to denote a leftward shift on the vertical axis, following [this nomenclature](https://xgcm.readthedocs.io/en/latest/grids.html#axes-and-positions).\n",
    "\n",
    "As a first example, we show zonal velocity shear in the top layer, which is the finite difference version of: \n",
    "\n",
    "$$ \\frac{\\partial u}{\\partial z}\\Big|_{z=-25m} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zonal_shear = grid.derivative(uvel_l,'Z')\n",
    "zonal_shear.isel(Z=0).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the underlying xgcm operations are: \n",
    "\n",
    "```\n",
    "grid.diff( uvel_l, 'Z' ) / ds.drW\n",
    "```\n",
    "\n",
    "Which is shown to be equivalent below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_result = (grid.diff( uvel_l, 'Z') ) /ds.drW\n",
    "xr.testing.assert_equal(zonal_shear, expected_result.reset_coords(drop=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A note on dimensions**: here we first interpolated from \"Z\"->\"Zl\" and \n",
    "the derivative operation shifted the result back from \"Zl\"->\"Z\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.  ('time', 'Z', 'YC', 'XG')\n",
      "2.  ('time', 'Zl', 'YC', 'XG')\n",
      "3.  ('time', 'Z', 'YC', 'XG')\n"
     ]
    }
   ],
   "source": [
    "print('1. ', ds.UVEL.dims)\n",
    "print('2. ', uvel_l.dims)\n",
    "print('3. ', zonal_shear.dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, the vertical profiles of horizontal average of zonal velocity \n",
    "and zonal velocity shear are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(1,2,figsize=(12,8))\n",
    "titles=['Horizontal average of zonal velocity, $u$',\n",
    "        'Horizontal average of zonal velocity shear, $\\partial u/\\partial z$']\n",
    "for ax,fld,title in zip(axs,[ds.UVEL,zonal_shear],titles):\n",
    "    \n",
    "    # Only select non-land (a.k.a. wet) points\n",
    "    fld = fld.where(ds.maskW).isel(time=0).copy()\n",
    "    grid.average(fld,['X','Y']).plot(ax=ax,y='Z')\n",
    "    ax.grid();\n",
    "    ax.set_title(title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, for meridional velocity and temperature in the top layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.derivative(vvel_l,'Z').isel(Z=0).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.derivative(theta_l,'Z').isel(Z=0).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note:** The `.derivative` function performs a centered finite difference operation. \n",
    "Keep in mind that this is different from \n",
    "[finite volume differencing schemes](https://mitgcm.readthedocs.io/en/latest/algorithm/finitevol-meth.html) \n",
    "as used in many ocean models.\n",
    "See [this section](https://xgcm.readthedocs.io/en/latest/example_mitgcm.html#Divergence) \n",
    "of documentation for some examples of how xgcm can be helpful in performing these operations.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metric weighted interpolation\n",
    "\n",
    "Finally, grid metrics allow us to implement area-weighted interpolation schemes quite easily. First, however, we need to once again define new metrics in the horizontal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['dxF'] = grid.interp(ds.dxC,'X')\n",
    "ds['dyF'] = grid.interp(ds.dyC,'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    ('X',): ['dxC', 'dxG','dxF'], # X distances \n",
    "    ('Y',): ['dyC', 'dyG','dyF'], # Y distances \n",
    "    ('Z',): ['drW', 'drS', 'drC'], # Z distances \n",
    "    ('X', 'Y'): ['rA', 'rAz', 'rAs', 'rAw'] # Areas \n",
    "}\n",
    "grid = Grid(ds, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show temperature interpolated in the X direction: from the tracer location to where zonal velocity is located, i.e. from `t` to `u` [in the horizontal view of the C grid shown here](https://xgcm.readthedocs.io/en/latest/grids.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.interp(ds.THETA.where(ds.maskC),'X',metric_weighted=['X','Y']).isel(Z=0).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This area weighted interpolation conserves tracer content in the horizontal, at least to first order \n",
    "as defined by the underlying interpolation operation. \n",
    "Note that in this example the difference between an area weighted interpolation and standard interpolation\n",
    "(i.e. arithmetic mean for first order) is quite small because the underlying field is smooth."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
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    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
